Course Content
Introduction
0/1
Introduction
03:31
Part 1. Python. 1st Dataset. PDF files. Tika OCR. Regular Expression. Array und Function
0/5
Python. Choosing python IDE. Anaconda. Install Python.
05:38
1st Dataset. PDF files. Tika OCR. Extracting content and metadata.
08:40
Regular Expression in Python. Pattern matching in Python with RegEx.
10:33
Array und Function in Python. Add data to Array. Create function.
09:48
Python & Regular Expressions
Part 1. Pandas DataFrame. Kaggle. Jupiter Notebook.
0/3
Pandas DataFrame. Two-dimensional size-mutable, tabular data structure.
09:15
Kaggle. Jupiter Notebook. Create an account. Plotting with matplotlib and seabor
11:35
Pandas & Kaggle
Part 1. Independent Work Tasks. 2nd Dataset.
0/2
2nd Dataset. Task. Data from PDF. Getting data from PDF drawings.
01:52
2nd Dataset. My solution.
05:39
Part 1. GitHub. Desktop GitHub
0/1
GitHub. Desktop GitHub. Store and manage code
07:03
Part 2. Python. PyTesseract OCR. Regular Expression. Array und Function.
0/3
Scanned PDF files. Convert a pdf document to images using Python.
10:49
Installing Tesseract. User-defined functions in Python
09:04
Python & Regular Expressions
Part 2. RegEx. Regular Expression in Python.
0/3
Regular Expression in Python. Pattern matching in Python.
09:02
Array und Function in Python. Add data to Array.
13:21
GeoPy – easy to locate the coordinates. Get the latitude and longitude of location
08:52
Part 2. Kaggle. Jupyter Notebook.
0/2
Kaggle. Jupiter Notebook. Plot data with matplotlib, seaborn, squarify.
10:58
Folium. Mapping in Python. Plot Geographic Data on a Map.
08:56
Part 3. Big Data Storage and MySQL.
0/2
Big Data Storage. Three ways to store digital data.
05:23
MySql. SQL. Introduction. How it works?
03:45
Part 3. Practice. Export Excel worksheet data to a MySQL table
0/3
Installing and Launching MySQL Workbench. How to Get Started with MySQL Workbench
03:29
Practice. Excel table into MySql. Import Excel data into a MySQL database.
08:16
MySQL Quiz
Part 3. A Storage System for Big Data. Hadoop.
0/1
Spark. Hadoop. Data’s Distribution. A Storage System for Big Data.
03:41
Part 3. Practice. How Apache Spark makes your slow MySQL queries 10x faster.
0/4
Installing and Launching Apache Spark. Download and Get Started.
05:07
Practice. Connecting Python To The Spark. Get Started with PySpark and Jupyter Notebook
07:11
Practice. Connecting MySQL with Spark. Export Data from Mysql to Spark.
09:33
Spark and Hadoop Quiz
Part 4. The Data Visualisation Tools. Introduction
0/1
Data Visualization Tools. Power BI, Tableau, Google Data Studio, Jupyter.
04:57
Part 4. Practice. Data Visualization with Python. Kaggle and Jupyter Notebook.
0/4
Python Data Visualizations. Prepare Data for Visualizations. (Part 1/3)
11:10
Python Data Visualizations. Clean data for Visualizations (Part 2/3).
04:57
Python Data Visualizations. Data Visualizations in Jupyter Notebook (Part 3/3).
11:19
Plotting in Python Quiz
Part 4. Online Data Visualization Tools. Introduction and getting started.
0/3
Practice. Power BI. Introduction and getting started.
07:00
Practice. Tableau. Introduction and getting started.
04:35
Practice. Google Data Studio. Introduction and getting started.
06:20
Part 5. Machine Learning. An Introduction.
0/2
What is machine learning? Key ML Terminology.
06:45
Practice. Predict the price of houses. Dataset 1. Beginner’s Guide to Jupyter
10:24
Part 5. Practice. How does machine learning work?
0/4
How does machine learning work? Prediction of construction time and cost.
07:13
Practice. Prediction of price and time. Data upload and preparation (Part 1/2)
08:57
Practice. Prediction of price and time. Evaluation Metrics (Part 2/2)
10:40
Machine Learning Quiz
Part 5. Workflow of a Machine Learning project.
0/1
Workflow of a Machine Learning project. Stages of the Machine Learning Modeling
05:11
Part 5. Practice. San Francisco – explore Building Permits Data. Build Predictive Model.
0/2
Practice. Data loading and preparation to Analyzing (Part 1/2).
12:32
Practice. Cost Prediction. Way to build a Predictive Model (Part 2/2).
14:08
Step-by-Step Full Course. All Parts. 📚 46 Lessons

📚 Additional materials on topics covered in the course:

Kaggle. The Beginner’s Guide
https://elitedatascience.com/beginner-kaggle

Jupyter Notebook: An Introduction
https://realpython.com/jupyter-notebook-introduction/

Delete rows/columns from DataFrame using Pandas.drop()
https://www.geeksforgeeks.org/python-delete-rows-columns-from-dataframe-using-pandas-drop/

Simple Scatter Plots
https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html

🔎 Topics covered in this course:

  • How do I upload a file to kaggle kernel?

  • How do you use kaggle dataset?

  • How to run Jupyter notebook using Kaggle kernels?

  • How to convert a CSV to dataframe in Python Jupyter Notebook?

  • How to use the functions of Pandas Dataframe?

  • How do I change the date format of a column in pandas?

  • How do I convert a string to datetime Objects in Python?

  • How to Calculate Difference Between Two Dates in Pandas Dataframe?

  • How do I delete a column in pandas DataFrame?

  • How do I add columns in  pandas DataFrame?

  • How do you visualize a dataset?

  • How do you plot a DataFrame in pandas?

Exercise Files
kaggle_notebook_data_from_pdf_drawings.zip
Size: 18.54 KB
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